ETC5521 Assignment 1

Global Fishing

This assignment is for ETC5521 Assignment 1 by Team EMU comprising of Min Min Soh, Rohan Baghel, Xiyun Zhou, and Zhang Zhang.

1 Data description

2 Expected findings

2.1 What would be the notable finding when we comparing the consumption with production?

2.1.1 Method

Data cleansing: Use left_join() function combine consumption.csv and capture_vs_farmed.csv into one data set named con_cap. In order to get the total production, add a new variable is the summation of Aquaculture production and Capture production. We use the new data set to do line plots and scatter plot for question one.

2.1.2 Visualization and analysis

Figure 2.1 depicts the growing trend in world seafood consumption from 1969 and 2018. The average yearly seafood consumption for one person was 13.8kg, while it increased to 20kg in recent time. Moreover, it is obviously that between 1990 and 1992, the average seafood consumption decreased, mostly because of economic problems in low-income nations such those in Africa, Latin America, the Caribbean, and the Near East. This resulted in increased pressure on many items’ prices (Dumas, 1992). Seafood consumption has grown globally as the crisis has subsided. Overall, this is consistent with our assumption that seafood consumption would continue to rise.

The line plot 2.2of world yearly seafood production shows an upward trend from 1969 to 2018. In 1970, the average production of seafood is 118 million metric tons. While the production became 3 times greater in recent year. And the production decreasing period happened same with the seafood consumption. We can make assumption that there has a positive relationship between seafood consumption and production.

Figure 2.1: Average seafood consumption in the world over time

Figure 2.2: Average yearly seafood production in the world over time

Figure 2.3: Contribution of top 10 seafood producer 2015-2018(metric tons)

The bar plot 2.3 shows the contributions of top 10 seafood producer by country. China is the largest contributor and provide 40% seafood of the whole world in recent years. Indonesia and India produce 11% and 5.6% respectively. It’s intriguing to note that except US produce 3% and Norway produce 1% of the seafood other major producers are from Asia.

Figure 2.4: Relationship between seafood consumption and production

To explore relationship between seafood consumption and seafood production, we made a scatter matrix plot 2.4. Since seafood produce in two ways: aquaculture and capture, we add another two variables Aquaculture production and Capture productionto do the scatter plot and try to find some interest between each variable. The outcome demonstrates a strong positive correlation between each variable. There is a significant linear positive relation between average consumption and total production as we expected.The correlation coefficient is 0.97 almost reach to 1. Moreover, the two methods used to produce fish also has a positive linear relation.

2.1.3 Summary and findings

1. Upward trend for seafood production and consumption worldwide
   
2. Positive linear relationship between seafood production and consumption.
3. Two seafood produce method: Aquaculture and capture have positive linear relation.
4. Asia is the major continent to produce seafood and China produces the most seafood.

2.2 Compare production of 7 different fish type. Which type of seafood contribute the most to meet the increasing demand?

2.2.1 Method

We want to explore something interest when comparing fish type production.

Use the contribution.csv data set and simplified the name of the variables of 7 types of fish, since they are too long and not easy to read. Add up each fish type as a total production variable. We wonder which country contribute the most for each type of fish. In order to to do that, I make a summary for average yearly production for each type of fish by countries and then using max() function to find the answer.

To compare production of each type fish, I decide to use column plot. Not only it can shows the trend but also easy to see the difference. Categorize year to 4 decade and get the average production for each type fish. Then use pivot_longer() to gather fish type into one column, this is the preparation for making the column plot.

Figure 2.5: Compare Fish Type production

## tibble [28 × 3] (S3: tbl_df/tbl/data.frame)
##  $ Decade          : chr [1:28] "1970-1980" "1970-1980" "1970-1980" "1970-1980" ...
##  $ Fish Type       : chr [1:28] "PelagicFish" "Crustaceans" "Cephalopods" "DemersalFish" ...
##  $ production(tons): num [1:28] 711866 61503 36897 516879 198112 ...

2.2.2 Visualization and analysis

From the column plot @ref:(fishtype) we can see that the Pelagic Fish has the largest production scale when comparing with other type fish. Demersal Fish used to take the second largest contribution of fish product. However, the second largest changed to Freshwater Fish in recent years. The overall trend for each type fish is increasing along with time. While the production of Pelagic Fish decreased in this decade and Demersal Fish almost keep a same level.

The table below shows the maximum yearly production of 7 different fish type by countries. Americas has the largest production for Pelagic Fish. Asia contribute most for Crustaceans, Cephalopods, Freshwater Fish, Molluscs and Marine Fish. And European countries contribute most of the Demersal Fish.

2.2.2.1 Maximum yearly production of 7 different fish type

Fish Type	Entity	production(metric tons)
Pelagic Fish	Americas	12495788
Crustaceans	Asia	3748006
Cephalopods	Asia	1578872
Demersal Fish	Europe	7979733
Freshwater Fish	Asia	14040488
Molluscs	Asia	6115966
Marine Fish	Asia	6597212

2.2.3 Summary and findings

1. Pelagic fish takes the largest fish production.
2. Asia contribute the most for fish production.

3 Conclusion

The seafood consumption and production keep increase with the time change. Moreover, it is noteable that a strongly positive linear relationship between fish consumption and production. China produces the most seafood in worldwide and Asia is the main continent producing seafood. Pelagic fish has the highest production scale and most of them comes from Americas.

Compared with 1960s, developing countries from South-east Asia take more seats in top 10 seafood producers in 2010s. Also, farmed fishing is the most important factor to the improvement overall productivity. Moreover, we observe that overexploited stock has strong positive correlation with world fish consumption.

4 References

Becker OScbRA, Minka ARWRvbRBEbTP, Deckmyn. A (2021). maps: Draw Geographical Maps. R package version 3.4.0, https://CRAN.R-project.org/package=maps.

C. Sievert. Interactive Web-Based Data Visualization with R, plotly, and shiny. Chapman and Hall/CRC Florida, 2020.

Dumas, M. W. (1992). Productivity trends: prepared fish and seafoods industry. Monthly Lab. Rev., 115, 3.

Hughes E (2022). tidytuesdayR: Access the Weekly ‘TidyTuesday’ Project Dataset. R package version 1.0.2, https://CRAN.R-project.org/package=tidytuesdayR.

H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

Ooms J (2022). gifski: Highest Quality GIF Encoder. R package version 1.6.6-1, https://CRAN.R-project.org/package=gifski.

Pedersen T, Robinson D (2020). gganimate: A Grammar of Animated Graphics. R package version 1.0.7, https://CRAN.R-project.org/package=gganimate.

Pedersen T (2020). patchwork: The Composer of Plots. R package version 1.1.1, https://CRAN.R-project.org/package=patchwork.

Pedersen T (2020). transformr: Polygon and Path Transformations. R package version 0.1.3, https://CRAN.R-project.org/package=transformr.

Simon Garnier, Noam Ross, Robert Rudis, Antônio P. Camargo, Marco Sciaini, and Cédric Scherer (2021). Rvision - Colorblind-Friendly Color Maps for R. R package version 0.6.2.

Simon Garnier, Noam Ross, Robert Rudis, Antônio P. Camargo, Marco Sciaini, and Cédric Scherer (2021). Rvision - Colorblind-Friendly Color Maps for R.

Subasinghe, R., Soto, D., & Jia, J. (2009). Global aquaculture and its role in sustainable development. Reviews in aquaculture, 1(1), 2-9.

Tierney N (2017). “visdat: Visualising Whole Data Frames.” JOSS, 2(16), 355. doi:10.21105/joss.00355 https://doi.org/10.21105/joss.00355, http://dx.doi.org/10.21105/joss.00355.

Urbanek S (2013). png: Read and write PNG images. R package version 0.1-7, https://CRAN.R-project.org/package=png.

Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, Grolemund G, Hayes A, Henry L, Hester J, Kuhn M, Pedersen TL, Miller E, Bache SM, Müller K, Ooms J, Robinson D, Seidel DP, Spinu V, Takahashi K, Vaughan D, Wilke C, Woo K, Yutani H (2019). “Welcome to the tidyverse.” Journal of Open Source Software, 4(43), 1686. doi:10.21105/joss.01686 https://doi.org/10.21105/joss.01686.

Wickham H, Hester J, Bryan J (2022). readr: Read Rectangular Text Data. R package version 2.1.2, https://CRAN.R-project.org/package=readr.

Wilke C (2021). ggridges: Ridgeline Plots in ‘ggplot2’. R package version 0.5.3, https://CRAN.R-project.org/package=ggridges.

Yihui Xie (2022). bookdown: Authoring Books and Technical Documents with R Markdown. R package version 0.26.

Zhu H (2021). kableExtra: Construct Complex Table with ‘kable’ and Pipe Syntax. R package version 1.3.4, https://CRAN.R-project.org/package=kableExtra.